To date, there has been considerable interest in detecting ship targets in airborne and spaceborne SAR. A direct statistical modelling of ship targets in SAR data is ideally preferred for ship detection, but it is challenging and complicated due to different ship types and structures. Consequently, sea clutter is chosen and modelled statistically instead, where all potential ship targets are detected in a reverse way by suppressing sea clutter. In the literature, numerous research works have been carried out for modelling sea clutter in airborne and spaceborne SAR data. These sea clutter statistical models use Gaussian statistics and non-Gaussian statistics. The so-called non-Gaussian statistics, such as K-distribution, log-normal distribution, Weibull distribution and others, were found to model sea clutter well, in particular for relatively high-resolution SAR data. Some relevant references are    Jakeman E. and Pusey P. N., 1976, A model for non-Rayleigh sea echo. IEEE Transactions on Antennas and Propagation, AP-24(6), pages 806-814,    Eltoft T. and Høgda K. A., 1998, Non-Gaussian signal statistics in ocean SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 36(2), pages 562-575, and    Ward K., Tough R., and Watts S., 2013, Sea Clutter: Scattering, the K distribution and Radar Performance. London: The Institution of Engineering and Technology. Second edition.
Often sea clutter modelling was conducted and reported by using only amplitude or intensity component rather than utilizing the complete scattering vector or polarimetric covariance matrix, which is available in fully polarimetric SAR data.
For ship detection, four main steps are generally involved, namely 1) land masking, 2) pre-processing, 3) prescreening, and 4) discrimination. The first step is to mask out land areas since only ships in the water are of interest. Moreover, this can help to reduce false alarms caused by land cover features. In the second step, image enhancement is carried out, which is optional provided that a constant false alarm rate detector is employed for the subsequent prescreening. Then, the prescreening step, which is the most crucial step, identifies potential ship pixels in the masked input image. The final step, that is discrimination, is to reduce the false alarm rate. For instance, the observation of a ship wake can be employed to confirm the presence of a moving ship.
In the literature, numerous prescreening algorithms have been proposed for the prescreening step. These can be generally grouped into two categories, namely global and local processing approaches. In the global processing approach, an image pixel is marked as a potential ship pixel if its intensity or test statistic is greater than a predefined global threshold. For example,    Lin, I-I, Kwoh, L. K., Lin, Y.-C., and Khoo, V., 1997. Ship and ship wake detection in the ERS SAR imagery using computer-based algorithm. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, vol. 1, pages 151-153,and    Liu, C., Vachon, P. W., and Geling, G. W., 2005. Improved ship detection with airborne polarimetric SAR data. Canadian Journal of Remote Sensing, 31(1), pages 122-131.applied this approach for ship detection in ERS SAR PRI and CV-580 SAR images, respectively. A careful threshold selection is prerequisite to successful ship detection by using this approach.
In the local processing approach, a local processing window is normally required. According to the local processing approach, the processing window consists of a test pixel, which is surrounded by a guard ring and then by a background ring. The design of the guard ring is to exclude a possible extended ship target from a background ring and, hence, the background ring contains purely sea clutter. Local processing approaches have received a great deal of attention in ship detection (see Zhang, F. and Wu, B., 2008. A scheme for ship detection in inhomogeneous regions based on segmentation of SAR images. International Journal of Remote Sensing, 29(19), pages 5733-5747 and Allard, Y., Germain, M., and Bonneau, O., 2009. Ship detection and characterization using polarimetric SAR data. In Shahbazian E., Rogova G., and DeWeert M. J. (Eds.), Harbour Protection Through Data Fusion Technologies (pages 243-250). Dordrecht: Springer). However, the restriction with these approaches, in particularly for very high-resolution SAR data, is that the window size needs to be varied accordingly with different sizes of ship targets in order to enable an efficient detection.